Emission Reduction via Fossil Fuel Subsidy Removal and Carbon Pricing, Creating Synergies with Revenue Recycling
Bibliographic record
Abstract
The removal of fossil fuel subsidies and the introduction of carbon pricing have been discussed for more than a decade, but their potential contribution to emission reduction is still uncertain, especially in relation to the potential indirect impact of revenue recycling. We have created a simulation model, GSI-IF, designed to assess the emission reduction potential resulting from removing fossil fuel subsidies and recycling part of the avoided subsidy and additional revenue from carbon pricing to renewable energy and energy efficiency. Our results show that emissions could decline by 7.1% in 2030 and up to 19.8% in 2050 compared to a baseline scenario. We find that subsidy removal is most effective in reducing emissions in countries with a high incidence of fossil fuel subsidies and it has stronger impact in the short term. The recycling of carbon pricing is most relevant for larger economies and its impact accumulates over time, generating growing GHG reductions year after year. In the current context (year 2022) with high energy prices, heavy stress on fiscal balances, and the renewed ambition of most governments to reduce emissions toward Net Zero in 2050, subsidy removal and carbon pricing hold promise in the toolbox of decarbonization options while improving fiscal sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".